Researchers introduce a set of universal relations that connect spectrum shapes to wave behavior in one-dimensional disordered systems. The framework reveals a never-before-seen critical state where waves localize differently depending on direction, and shows how it can be tracked using a topological winding number.
Researchers have successfully solved a problem in graph theory that has attracted attention from within the field. The team's research involves packing coloring, which deals with labelling parts of a graph to comply with certain rules and avoid specific conflicts.
Researchers found that neural networks grown in a dish can predict future stimulus events, with higher prediction efficiencies in focally stimulated networks. Focal electrical stimulation induced long-term memory traces and reduced dependence on short-term memory.
Researchers at Johns Hopkins developed a mathematical method to predict the effectiveness of catheter ablation for treating Afib. The study found that improving electrical communication in the heart immediately after the procedure can predict longer-term success rates.
Scientists have created a propagation-based algorithm to extract both link-density and link-pattern communities from real-life networks. This approach outperforms existing state-of-the-art algorithms in detecting real-life communities, particularly those characterized by internal patterns of similar connectedness.
Researchers have discovered a new phenomenon in which super-connectivity is significantly delayed due to the introduction of randomness. The transition becomes instantaneous once it occurs, like crystallizing ice.